Skill UI

Generates realistic database seed data and scripts for development or testing, leveraging Faker libraries to respect schema constraints, maintain relationships, and allow configurable volumes for quick population of demo or test environments.

A comprehensive skill designed to manage, track, and persist image assets generated by Ideogram's API. This tool addresses the critical issue of expiring image URLs by implementing a complete lifecycle pipeline. It handles metadata tracking (prompt, seed, model, style, etc.), immediate downloading, and robust storage to both local filesystems and cloud solutions like S3. Features include asset persistence, generation history logging, and image reproduction using stored seed values.

This comprehensive guide details the complete workflow for setting up and maintaining a local Supabase development environment. It utilizes Docker and the Supabase CLI to run the entire full-stack service (Postgres, Auth, Storage, etc.) locally. It guides developers through initialization, creating version-controlled migrations using diff, seeding data, and performing clean database resets, ensuring local iteration accurately mirrors production capabilities.

Scaffolds an empty xUnit integration test project targeting Oracle in .NET solutions, including a transaction-rollback base class and seed data manager. Use in Phase 3 before writing Oracle baseline tests for Oracle-to-PostgreSQL migration.

This guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.

Provides a comprehensive setup guide for running ClickHouse locally using Docker Compose. This includes defining the database schema, seeding mock data, and setting up robust integration tests (using vitest). Ideal for developers building OLAP systems or performing local data analysis to ensure schema and query reliability.

A comprehensive 10-page pitch deck template designed for fundraising and presenting to venture capitalists (VCs). It covers all essential sections, including problem identification, solution overview, market size (TAM/SAM/SOM), traction data visualization, business model, go-to-market strategy, and the funding ask. Ideal for startups seeking seed or Series A funding.

This skill helps researchers strengthen AAAI paper submissions by ensuring reproducibility standards are met. It covers experimental traceability, seed reporting, compute disclosure, and dataset licensing. Users learn to map claims to evidence, audit checklists, and prepare artifacts for Phase-1 reviewers to validate rigor across AI scopes.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...